Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts

Abstract

Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.

Rest functional brain maturation during the first year of life

“…Globally, our findings are in accordance with results from prior research based on histology, structural and rest functional brain imaging that has revealed distinct maturation trajectories of cortical regions and brain networks over the first year of life (10, 24, 33, 50). Firstly, at the histological level, post-mortem data showed that the time course of synaptogenesis differs across cortical regions.…”

Relating anthropometric indicators to brain structure in 2-month-old Bangladeshi infants growing up in poverty: a pilot study

“…Although only segmentations of relatively high quality were used, these segmentations were still of low quality relative to segmentations produced from structural images in adults and older children; this is perhaps not surprising as the cortex of an infant's brain is thin and the contrast between gray and white matter is low. The difficulty of infant segmentation is well known (Makropoulos et al, 2016) and acknowledgment of this has led to nonconformity in infant segmentation pipelines (Makropoulos et al, 2018;Zhang et al, 2019). With regard to anthropometry, infant weight, for instance, can vary considerably depending on quantities eaten and recency of quantities expelled.…”

“…3A) were submitted to the FreeSurfer recon-all pipeline for infant MRI images (Zöllei et al, 2017) and subject data. It should be noted that there is still no consensus regarding the best segmentation pipeline for infant imaging (Makropoulos et al, 2018;Zhang et al, 2019).…”

The relationship between biological and psychosocial risk factors and resting‐state functional connectivity in 2‐month‐old Bangladeshi infants: A feasibility and pilot study

“…Deformable Registration via Attribute Matching and Mutual‐Saliency Weighting (DRAMMS) (Ou, Sotiras, Paragios, & Davatzikos, ) was used as part of registering atlas and subject data. It should also be noted, however, that there is still no consensus about the segmentation pipeline for infant imaging (Makropoulos, Counsell, & Rueckert, ; Zhang, Shen, & Lin, ). These segmentations were subsequently consolidated into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) regions‐of‐interest (ROIs) for each participant using in‐house MATLAB 2015b ( MathWorks ) code.…”